A novel mth best average biased received power (ABRP) user association (UA) scheme is proposed for K-tier heterogeneous networks, where a user is associated with the closest base station (BS) of the kth tier having the mth strongest ABRP, . By using stochastic geometry and Poisson point processes, the authors perform the analysis of the UA probability that a typical user is associated with a BS that has the mth strongest ABRP as well as the downlink signal-to-interference-noise ratio (SINR) coverage performance. The obtained results show that, for a given tier k, when the layer's position in the ordered set of ABRPs is not equal to m and the power or density of BSs is relatively small, i.e., , the UA probability increases monotonically with or . Contrarily, it decreases. For the other UA probabilities, the opposite results are achieved. Additionally, the results also show that the coverage probability of the downlink SINR of the tier k always increases with power , which is consistent with the one in the conventionally best ABRP scheme. However, for other tiers, the corresponding downlink SINR coverage probabilities do not behave monotonically with the power .

Relay stations (RSs), as key components of next-generation heterogenous networks, can be readily deployed between base station (BS) and users to extend cell coverage and improve throughput. Since RSs are connected with a donor BS through wireless backhaul-links, i.e. BS–RS links, additional radio resources should be allocated to these links. For in-band relaying, access-links and backhaul-links should be jointly optimised, which make the resource allocation problem for a relay network more difficult and challenging. In this study, the authors develop an analytical framework for resource allocation in relay networks with a time-division duplexing mode for access-link and backhaul-link transmissions. The solution of the proposed analytical framework maximises the total utility of the relay network, and interference coordination among the BS and RSs is also considered. The developed framework is thoroughly evaluated by comparison with exhaustive search methods in different case studies. The comparison results show that the proposed framework provides optimal solutions with low computational complexity.

In this study, non-orthogonal multiple access (NOMA)-based multiple-antenna and multiple-relay networks over Nakagami-m fading channels with imperfect channel state information (CSI) and successive interference cancellation (SIC) error are investigated. In order to measure the performance of the system, the exact outage probability and the ergodic sum rate of the system are explored. By analysing the impact of the partial relay selection scheme, it is shown that the performance gain can be achieved significantly by increasing the number of relays from one to two, but when the number of relays is equal to or greater than three, the increment of the performance gain becomes smaller and smaller, and finally saturated as the number of relays increases. Furthermore, due to the complexity of the derivation process for the ergodic sum rate, an upper bound of the ergodic sum rate is taken into account. Finally, by carrying out a series of experimental simulations, the authors prove the correctness of the theoretical derivations above, and from the comparison of NOMA and orthogonal multiple access (OMA), it is found that NOMA can provide better spectral efficiency and user fairness than OMA.

We experimentally demonstrate a multicarrier interleave division multiple access (IDMA) scheme for bidirectional visible light communications using orthogonal frequency division multiplexing (OFDM), which has the advantages of good tolerance against multi-path, high spectral efficiency and excellent transmission performance. The generated IDMA signals are modulated on OFDM subcarriers and the different users are separated by the interleavers. When compared with orthogonal frequency domain multiple access, IDMA-OFDM offers increased transmission spans of 30 and 50 cm at a bit error rate (BER) of 10⁻³ in the cases of two and four users, respectively.

In this study, the authors analyse the outage performance of the indoor relay-assisted hybrid radio frequency (RF)/visible light communication (VLC) systems. They derive closed-form expressions for the outage probability of the end-to-end signal-to-noise ratio. Then they obtain the optimal approximation parameter with optimising the outage probability using the differential evolution algorithm. Moreover, they analysed the average bit error rate (BER) performance of the hybrid RF/optical system using pulse position modulation method while assuming the timing error in synchronisation. Finally, they present some numerical results utilising the newly derived exact closed-form expressions.

The spectrum-monitoring is a prominent technique to detect the reappearance of the primary user (PU) during the cognitive users’ (CUs) data transmission. However, the imperfect spectrum monitoring (SM) causes a delay in the identification of PU that introduces interference at PU as well as data-loss. The cooperation among CUs for SM is a proficient solution to diminish these impairments of imperfection. Thus, in this study, a scenario of the heterogeneous cognitive radio network (CRN) is presented in which the CUs perform cooperation among them for SM and have analysed the impact of cooperation on several performance metrics such as the data-loss, interference efficiency and energy efficiency. An algorithm is presented for the evaluation of data-loss for different circumstances of the traffic intensity of PU and probability of SM error in the proposed heterogeneous CRN. In addition to this, the closed-form expressions for the cooperative and non-cooperative SM of these metrics are derived. Furthermore, the simulation results are illustrated for different state-of-affairs of the probability of SM error and traffic intensity.

In the past few years, peer-to-peer (P2P) live streaming systems have gained great commercial success and have become a popular way to deliver multimedia content over the Internet, which received more and more attentions from both industry and academia globally. However, the dramatic rise in popularity makes these systems more likely to be vulnerable targets. In this study, mesh-pull infrastructure architecture and pollution attack principle for P2P live streaming systems were presented firstly, and then the various user behaviours under the pollution attack were analysed. Subsequently, the authors proposed an analytical modelling framework of content pollution attack for P2P live streaming systems. Different from the existing content pollution propagation models, it considers the impact of user behaviours in the attack. Furthermore, to ensure the availability and accuracy of the model, the real-world experimental attack data for a popular commercial system was used to verify it. The results showed that the model is a feasible and efficient tool to analyse and predict content pollution propagation in real-world P2P live streaming systems. The authors' work can provide an in-depth understanding of the content pollution propagation in P2P live streaming systems, and evaluation of restraining illegal content distribution for copyright holders and government.

Waveform design plays an important role in compressed sensing radar (CSR). The performance of the target scene recovery in CSR scenarios depends on the mutual coherence of the sensing matrix, which is determined by the radar transmission waveform. In this study, the authors propose a waveform optimisation method for pulse-train scenario through solving the matrix nearness problem between the Gram matrix and the identity matrix. This method has a simple closed-form solution and simulation results show that the proposed method is more efficient than the current waveforms such as linear frequency modulation (LFM), Gaussian and Alltop sequences in terms of mutual coherence, recovery errors and recovery percentages.

In the Internet of Things, wireless sensor networks (WSNs) are widely deployed. In recent years, wirelessly powered WSNs or battery-free WSNs (BF-WSNs), in which the nodes harvest energy from radio signals in the environment, have been emerging to support sustainable operation for WSNs. It is significant to design an energy-efficient data delivery scheme for the BF-WSNs. In this study, the authors propose the prefix code based scheme (PCBS) to save energy in data delivery by making use of the energy consumption disparity (ECD) between transmitting/receiving bit 0 and bit 1 in the existing non-modulation baseband transmission or carrier-modulation based passband transmission. The authors formulate an optimisation problem and use genetic algorithm to find its solution so that the energy-efficient prefix codebook is obtained. The codebook dilutes the ECD by containing the energy-consuming bit as few as possible, and the PCBS maps each m-bit data block into a prefix codeword in the codebook to conduct energy-efficient transmission at the transmitter and vice versa at the receiver. Both the experiments on wireless identification sensing platform and the simulations demonstrate that the proposed PCBS outperforms the existing schemes in terms of energy saving.

Solar energy harvesting constitutes an attractive solution to provide energy for communicating objects. The advantage of this energy over other forms of renewable energy is that the available solar power can be predicted with reasonable accuracy, which allows the implementation of efficient power management techniques. Nevertheless, harvested solar energy comes from a non-controllable source. Therefore, the prediction of the solar radiation and energy availability is a critical issue, as the amount of the harvested energy may vary over time. In this study, a novel solar radiation and energy predictor (Solar Energy Predictor for Communicating Sensors: SEPCS) is proposed. This prediction model uses past energy observations to forecast future energy availability in short term. To assess the performance of the proposed algorithm, authors used database providing the solar radiation evolution for one year. Then, a comparative performance evaluation shows that the SEPCS predictor significantly outperforms the state-of-the-art energy predictors, by decreasing the average prediction error from 28 to 6.5%.

In this study, the bit error rate (BER) performance of multiple-input-multiple-output (MIMO) hierarchical quadrature amplitude modulation systems employing maximum ratio transmission (MRT-HQAM) is investigated under the effects of imperfect channel state information (ICSI) due to the channel estimation error and feedback delay. Theoretical expressions for the BER performance of the MRT-HQAM systems are derived using the two common ICSI models that exist in the current literature, specifically when studying the MIMO beam-forming systems with ICSI, where the error term due to the ICSI is assumed to contribute to either the noise component or to the signal component. The BER results for the two ICSI models are presented and then compared. In addition, various antenna configurations are studied to illustrate the performance gains achievable with additional antennas, and to identify the ideal antenna configuration for a given number of antennas. Moreover, the hierarchical modulation parameter is varied to determine the varying levels of sensitivity of the hierarchical layers in the presence of ICSI. The theoretical BER expressions provided for both ICSI models are verified by simulation results.

In this study, a hybrid modulation technique called PPM–GMSK, based on the pulse position modulation (PPM) and phase-sampled Gaussian minimum shift keying (GMSK) technique, is investigated by considering log-normal and gamma–gamma turbulence as the channel-fading statistics. The analytical bit error rate (BER) and outage probability of the proposed hybrid modulation are derived. The results show that the hybrid modulation technique is an efficient technique to improve the performance of optical wireless communication for a cellular backhaul channel. Compared to 2-PPM and minimum shift keying (MSK), the proposed hybrid scheme has a less BER and a reduced amount of outage probability. Moreover, to achieve a reliable voice communication at 2 Gbps bit rate and BER of under a weak turbulence effect, the PPM–GMSK has a coverage distance of about 3 km, whereas for PPM and MSK it is 2.6 and 2.4 km, respectively.

Fog computing is a distributed computing model that can utilise the storage, analysis and processing capabilities of fog nodes near edge devices. Although fog computing can support task processing for various Internet of Things (IoT) systems, Fog-IoT architecture faces several new challenges with the rapid development of IoT systems, especially delay-sensitive IoT systems, such as stochastic and dynamic data arrival, optimal utility and deadline of tasks. To address these challenges, workload scheduling toward worst-case delay and optimal utility for single-hop Fog-IoT architecture are studied and the workload dynamic scheduling algorithm (WDSA) is proposed. The proposed WDSA algorithm can maximise the average throughput utility while guarantees the worst-case delay of task processing. In addition, it is online and needs no prior information about future. The algorithm performance is analysed from the perspective of optimality and worst-case delay, demonstrating that the proposed WDSA algorithm can get an approximate optima and worst-case delay guarantees. Finally, simulation results demonstrate that the efficiency and efficacy of this kind of the algorithm can meet the requirement.

The pilot contamination problem creates a limitation to the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate the pilot contamination, in this study, the authors propose a novel channel estimation for massive MIMO systems, using sparse Bayesian learning (SBL) based on a pattern-coupled hierarchical Gaussian framework. In the proposed technique, the sparsity of each channel coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the channel coefficients can be estimated more efficiently in contrast to the conventional channel estimators in terms of channel estimation with pilot contamination. Furthermore, they derive the mean square error (MSE) analytical expression for the proposed technique and based on that MSE expression, a pilot design criterion is proposed to design the optimal pilot to improve the estimation accuracy of the proposed algorithm using the Lagrange multiplier optimisation method. Results show that they can reduce the MSE of the SBL estimator by employing the optimal pilot sequence.

Visible light communications have recently gained increasing attention as it is appealing for a wide range of applications such as indoor positioning. However, the light-emitting diode-based indoor positioning systems suffer from multipath distortion inside a room, leading to high positioning error. In order to mitigate the effect of multipath distortion of the optical channel, an indoor visible light positioning algorithm combined with ellipse-based asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) is proposed in this study, wherein the real-valued output of orthogonal frequency division multiplexing is modulated onto an ellipse, and only the imaginary value from the complex point on the ellipse is transmitted. Moreover, a received-signal-strength technique is used to determine the location of the receiver, and the Trust-region technique is employed to realise the 3D positioning. Simulation results demonstrate that this proposed algorithm can achieve high positioning accuracy, and the impact of different system parameters on the positioning accuracy is further investigated compared with the systems based on ACO-OFDM.

This paper considers a multiuser multiple-input multiple-output (MIMO) space-time block coded system that operates at a high data rate with full diversity. In particular, they propose to use a full rate downlink algebraic transmission scheme combined with a differential space-time scheme for multiuser MIMO systems. To achieve this, perfect algebraic space-time codes and Cayley differential transforms are employed. Since channel state information (CSI) is not needed at the differential receiver, differential schemes are ideal for multiuser systems to shift the complexity from the receivers to the transmitter, thus simplifying user equipment. Furthermore, orthogonal spreading matrices are employed at the transmitter to separate the data streams of different users and enable simple single user decoding. In the orthogonal spreading scheme, the transmitter does not require any knowledge of the CSI to separate the data streams of multiple users; this results in a system which does not need CSI at either end. With this system, to limit the number of possible codewords, a sphere decoder is used to decode the signals at the receiving end. The proposed scheme yields low complexity transceivers while providing full rate full diversity with good performance. Simulation results demonstrate the effectiveness of the proposed scheme.

As a hotspot of wireless communication, vehicular communication is playing an important role in the smart city. This study focuses on vehicle-to-vehicle (V2V) radio channel characteristics at 5.9 GHz band based on measurement, under a ramp with soundproof wall on the urban viaduct and an urban intersection scenario, which are two similar but with slight difference propagation environments. Differences of V2V radio channel characteristics between two scenarios have been measured and analysed, in the view of best fit distribution of fades, power delay profile, delay spread, channel gain and Doppler spread. It is found that Weibull and Ricean distribution has a good match in non-line-of-sight and line-of-sight region, respectively. Transformation of different propagation mechanisms can increase or reduce the received power by 9–11 dB on average. Also, the power delay profile and root mean square delay spread are influenced by the different propagation conditions. Moreover, Ricean K-factors and Weibull shape parameters are modelled based on the least-square. The comparison of channel properties between these two classical urban scenarios is vital to the optimal design and performance of V2V communication system.

A novel robust adaptive beamforming algorithm based on a coprime array is proposed in this study. First, the authors exploit the virtual array structure of the coprime coarray to construct two subspaces. The first subspace is obtained from the covariance matrix of the virtual uniform linear array, whereas the second one is obtained by integrating a spatial spectrum over each angular sector of the signal. Then, by using a closed-form expression of the projection-based method, the steering vectors (SVs) of the signals are estimated from the intersection of the two subspaces. Moreover, according to the covariance fitting theory, the power associated with the desired signal, interference signals, and noise is obtained from the virtual sample covariance matrix. In addition, to reconstruct the interference-plus-noise covariance matrix (INCM), the maximum correlation principle is used to transfer the virtual SVs of the signals to real ones corresponding to a physical array. Finally, with the estimated SV of the desired signal and INCM, the proposed robust algorithm is devised. Simulation results demonstrate the superiority and effectiveness of the proposed algorithm.